Sepsis represents a large and increasing burden on the US health care system. The incidence of sepsis is greater than 3.0 per 1,000 population and case fatalities approach 20%, accounting for more than $17 billion annually in direct medical costs. Recent advances in sepsis therapy, such as time-sensitive antibiotic administration and fluid resuscitation, require prompt diagnosis, and in some cases, transfer to referral centers for definitive therapy. Until now, such early recognition occurs after patients arrive at hospitals, when a critical window for treatment and referral may already have passed. An alternative approach may be to diagnose and treat sepsis in the pre-hospital period (which, as shown in preliminary work, is often of considerable duration). Emergency medical services (EMS) systems already play a key role in the pre-hospital management of acute cardiovascular disease, stroke, and trauma, conditions that also benefit from care at optimal centers with advanced warning. Although EMS transports over twice as many cases of sepsis as of acute cardiovascular disease, there are no prehospital tools to identify or manage sepsis, potentially leading to missed opportunities, and avoidable deaths.16 Key to effective pre-hospital care and triage decisions is the need to estimate both the probable diagnosis and the severity of illness. My proposed research project addresses this problem by coupling clinical prediction modeling with cutting edge biomarker science to identify and risk stratify prehospital patients for treatmen of sepsis. I take a novel approach by simultaneously integrating diagnostic and prognostic information for sepsis using clinical data and biomarkers. The main portion of the research focuses on existing, widely available clinical and biologic data. However, recognizing the rapid growth of novel biomarker discovery platforms, I have also included an exploratory aim using an agnostic, global metabolomics approach. The proposed research is organized as three aims. Aim #1 will develop an integrated model for sepsis and mortality risk using a large, existing prehospital clinical database. Aim #2 will determine the incremental knowledge gained from individual and combinations of candidate prehospital biomarkers for sepsis and mortality risk in a modest prospective study. Aim #3 will explore novel mass-spectrometry-based, metabolomic biomarkers of prehospital sepsis and mortality using a global, agnostic discovery approach in a small nested subset of the Aim #2 cohort. I will conduct this research under the guidance of my mentor Dr. Derek Angus and a team of expert advisors with whom I have established relationships. I will also complement the research with a didactic training program focusing on: 1) advanced biostatistics of risk modeling, 2) the methodologic tools necessary to conduct rigorous biomarker analysis and discovery, and 3) the practical, ethical, and management skills to perform prospective, prehospital research. Together, the didactic training and research project are designed to provide me with the knowledge and skills critical for successful inter-disciplinary research that translates insights from biomarker and risk modeling into a strategy for prehospital recognition of sepsis. My goal is that these experiments will set the stage for future NIH-funded trials of patient and system-level interventions in prehospital sepsis. I submit that this investment in my career development will contribute to new knowledge regarding the mechanism underlying nascent sepsis and the predictive role of prehospital clinical data and biomarkers, and has the potential to improve the emergency care of our highest acuity, most resource-intensive patients.